Since the model's learnt latent-space is key for TRUST-LAPSE performance, we visualize the latent-space embeddings of the coreset and incoming samples using the UMAP [1]} tool. As an example, we take our audio dataset and
plot the UMAP embeddings of our raw InD coreset data and their extracted latent-space representations from the trained encoders in Fig. REF , left (UMAP plots for other domains in Appendix, Figs. REF , REF , REF ). We see that the raw data are too high-dimensional to be captured according to their class labels by UMAP. On the other hand, the extracted representations arrange themselves in well-separable clusters (Fig. REF , middle) showing the representational capacity of the trained encoder.
To a certain extent, the distance score and the similarity score can be visualized in the UMAP embeddings as well for first level debugging, but it is important to note that it is not necessarily distance preserving; it is just a 2D projection of a much higher dimensional latent-space and is bound to be noisy.
